250
The calculation of the solar incidence uses the three-dimensional knowledge
acquired automatically by SAGA GIS from the input raster file: knowing the
latitude and longitude supplied by the georeferenced raster file, the algorithm
simulates the height of the sun and considers the dimensioned urban elements,
projecting shadows, and thereby calculating the potential solar incidence for
each pixel.
The output will therefore still be a raster file with a 0.5 m
2 per pixel resolution, where each pixel contains the information in kWh. The process has the
option to choose the time of year to be analyzed (January rather than July),
and the number of days (1–365).
Step Three
The final step of the analysis compares the information obtained so as to
identify the urban areas vulnerable to strong solar radiation, in order to come
up with more accurate mitigating and/or adaptive solutions.
Making use of any GIS Software (for this example we used Quantum GIS)
it is possible to analyze quantitatively the two dimensions under study.
As for identifying the roofs most suitable for a photovoltaic or solar thermal installation, one can simply evaluate information such as solar incidence,
inclination angle, and slope size.
As for identifying hot urban areas (with greater solar incidence), one can
simply ask Quantum GIS for the location of areas vulnerable to solar incidence (it will be better to convert the raster file into vector format for this
purpose). The resulting zoning information will be of great help in choosing
an intervention, where, depending on land area type (square, parking lot, sidewalk, intermodal station, etc.), this can be done with greater accuracy and
solutions pertaining to shading in conjunction with renewable energy production can be easier to find.
Bibliography
Hirschmuller, H. (2008). Stereo processing by semi-global matching and
mutual information. IEEE Transaction on Pattern Analysis an Machine
Intelligence, 30(2), 328–341.
Musco, F. (2008). Cambiamenti Climatici, politiche di adattamento e mitigazione: una prospettiva urbana, in Asur, Franco Angeli, Milan.
Musco, F., Maragno, D., Gariboldi, D., & Vedovo, E. (2013). Remote Sensign
e Cambiamenti Climatici: rischi e opportunità nel riuso e riciclo delle città.
(continued)
F. Musco et al.
The calculation of the solar incidence uses the three-dimensional knowledge
acquired automatically by SAGA GIS from the input raster file: knowing the
latitude and longitude supplied by the georeferenced raster file, the algorithm
simulates the height of the sun and considers the dimensioned urban elements,
projecting shadows, and thereby calculating the potential solar incidence for
each pixel.
The output will therefore still be a raster file with a 0.5 m
2 per pixel resolution, where each pixel contains the information in kWh. The process has the
option to choose the time of year to be analyzed (January rather than July),
and the number of days (1–365).
Step Three
The final step of the analysis compares the information obtained so as to
identify the urban areas vulnerable to strong solar radiation, in order to come
up with more accurate mitigating and/or adaptive solutions.
Making use of any GIS Software (for this example we used Quantum GIS)
it is possible to analyze quantitatively the two dimensions under study.
As for identifying the roofs most suitable for a photovoltaic or solar thermal installation, one can simply evaluate information such as solar incidence,
inclination angle, and slope size.
As for identifying hot urban areas (with greater solar incidence), one can
simply ask Quantum GIS for the location of areas vulnerable to solar incidence (it will be better to convert the raster file into vector format for this
purpose). The resulting zoning information will be of great help in choosing
an intervention, where, depending on land area type (square, parking lot, sidewalk, intermodal station, etc.), this can be done with greater accuracy and
solutions pertaining to shading in conjunction with renewable energy production can be easier to find.
Bibliography
Hirschmuller, H. (2008). Stereo processing by semi-global matching and
mutual information. IEEE Transaction on Pattern Analysis an Machine
Intelligence, 30(2), 328–341.
Musco, F. (2008). Cambiamenti Climatici, politiche di adattamento e mitigazione: una prospettiva urbana, in Asur, Franco Angeli, Milan.
Musco, F., Maragno, D., Gariboldi, D., & Vedovo, E. (2013). Remote Sensign
e Cambiamenti Climatici: rischi e opportunità nel riuso e riciclo delle città.
(continued)
F. Musco et al.